Protecting encoded data slice integrity at various levels

ABSTRACT

A method for execution within a dispersed storage network (DSN), where the method begins by calculating, utilizing a first integrity check value function, an integrity check value of a first type for each encoded data slice of a set of encoded data slices to produce a corresponding set of integrity check values. The method continues by issuing, via a network, one or more sets of write slice requests 1-n to a set of storage units 1-n within the DSN, where the one or more sets of write slice requests include a plurality of sets of the encoded data slices and a corresponding plurality of sets of the integrity check values. The method continues, when verifying integrity of a received encoded data slice, by a storage unit calculating, utilizing a second integrity check value function, an integrity check value of a second type for the encoded data slice.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/287,145,entitled “VERIFYING INTEGRITY OF ENCODED DATA SLICES,” filed Jan. 26,2016, which is hereby incorporated herein by reference in its entiretyand made part of the present U.S. Utility Patent Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of protectingencoded data slice integrity at various levels in accordance with thepresent invention;

FIG. 9A is a logic diagram of an example of a method of protectingencoded data slice integrity at various levels in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In one embodiment, a DS processing unit (DSN processing unit), whenstoring a slice, may compute an integrity check value (ICV) of theslice, and for greatest protection effect, may compute the integritycheck value over not just the slice data, but other relevant (andimportant) characteristics of the slice, such as its name, and revisionnumber. For example, this value may be computed as(slice_name∥revision∥slice_data). The slice, slice name, and revision,along with this ICV are then sent in the write request to the DSNstorage unit. The DSN storage unit then verifies the ICV by recomputingit from the received values and comparing it to the received ICV. Ifthey do not match, the DSN storage unit will return an error indicatingthe request was corrupted. If it matches, the DSN storage unit may storethe slice and store the received ICV, it may store just the slice, or itmay store the slice and store a new ICV computed using a differentalgorithm.

For example, the DS processing unit may have the capabilities to computethis value efficiently, but the DSN storage unit may not. Therefore, theDSN storage unit may compute and store an ICV which it can moreefficiently verify. This is important as the DSN storage unit may needto re-verify the ICV many times (on every read, or periodically fromtime to time). Another reason to compute a new ICV is that someintegrity check algorithms may be more suited for detecting networkcorruption, or the types of corruptions that commonly occur to data whentransmitted over a network, compared to the forms and types ofcorruptions that are more common on storage media. During a read, theDSN storage unit may optionally return the stored or a re-computed ICVto the DS processing unit. Note that the ICV may need to be sent with acorresponding algorithm identifier, which indicates how it was computed(if there is more than one possibility for how it was computed).

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a DSN processing unit such asdistributed storage and task (DST) processing unit 16 (computing unit)of FIG. 1, the network 24 of FIG. 1, and a set of storage units 1-n.Each storage unit may be implemented utilizing a DSN storage unit suchas storage unit 36 of FIG. 1. The DSN functions to verify integrity ofencoded data slices.

In an example of operation of the verifying of the integrity of theencoded data slices, the DST processing unit 16 calculates, utilizing afirst integrity check value function, an integrity check value of afirst type for each encoded data slice of a set of encoded data slicesto produce a corresponding set of integrity check values, where data(e.g., data A) is divided into a plurality of N data segments, and whereeach data segment is dispersed storage error encoded to produce a set ofencoded data slices (e.g., 1-n) of a plurality of sets of encoded dataslices. The calculating includes one or more of selecting the firstintegrity check value function (e.g., based on a predetermination,interpreting system registry information, interpreting a request,interpreting available processing resources), performing a correspondingselected one-way deterministic function (e.g., a hashing function, ahash-based ethics authentication code function, a sponge function, amask generating function) and one or more of the encoded data slice, aslice name of the encoded data slice, and a revision number of theencoded data slice to produce the corresponding integrity check value.

Having calculated the integrity check values of the first type, the DSTprocessing unit 16 issues one or more sets of write slice requests 1-nto the set of storage units 1-n, where the one or more sets of writeslice requests include the plurality of sets of encoded data slices andthe corresponding plurality of sets of integrity check values. Forexample, the DST processing unit 16 generates each write slice requestto include one or more encoded data slices, one or more correspondingintegrity check values, and an indicator to identify the first integritycheck value function; and sends, via the network 24, the write slicerequests to the set of storage units.

When verifying integrity of the received encoded data slice, a storageunit calculates, utilizing a second integrity check value function, anintegrity check value of a second type for the encoded data slice. Theverifying includes calculating the integrity check value of the firsttype for the received encoded data slice to produce a calculatedintegrity check value and indicating a favorably verified encoded dataslice when the calculated integrity check Valley substantially matchesthe corresponding received integrity check value.

The calculating of the integrity check value of the second type includesthe storage unit selecting the second integrity check value function(e.g., based on one or more of a predetermination, interpreting thesystem registry information, interpreting a request, interpretingavailable storage unit processing resources), performing a correspondingselected one-way deterministic function on one or more of the receivedencoded data slice, the received slice name of encoded data slice, thereceived revision number of encoded data slice to produce thecorresponding integrity check value of the second type, and storing theintegrity check value of the second type. For example, the storage unit3 performs the corresponding selected one-way deterministic function onencoded data slice 3-2, a slice name of the encoded data slice 3-2, anda revision number of the encoded data slice 3-2 to produce the integritycheck value 3-2 for storage within the storage unit 3.

When the data is to be recovered, each storage unit issues a readresponse to the DST processing unit 16 when the storage unit verifiesintegrity of the recovered encoded data slice utilizing the integritycheck value of the second type. The issuing includes retrieving theencoded data slice, and recalculating the integrity check value of thesecond type, indicating favorable verified integrity when therecalculated integrity check value of the second type substantiallymatches a retrieved integrity check value of the second type, generatingthe read response to include the retrieved encoded data slice, theintegrity check value of the second type, and an indicator to identifythe second integrity check value function, and sending, via the network24, the read response to the DST processing unit 16, where the DSTprocessing unit 16 dispersed storage error decodes a decode thresholdnumber of received verified encoded data slices for each set of encodeddata slices to reproduce the data.

FIG. 9A is a flowchart illustrating an example of verifying integrity ofencoded data slices. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-2, 3-8, and also FIG. 9. The method includes astep 902 where a processing module (e.g., of a distributed storage andtask (DST) processing unit) calculates an integrity check value of afirst type for each encoded data slice of a set of encoded data slicesutilizing an first integrity check value function to produce acorresponding set of integrity check values, where data is divided intoa plurality of data segments, and where each data segment is dispersedstorage error encoded to produce a corresponding set of encoded dataslices of a plurality of sets of encoded data slices. The calculatingincludes selecting the first integrity check value function andperforming a corresponding selected one-way deterministic function onone or more of the encoded data slice, a slice name of the encoded dataslice, and a revision number of encoded data slice to produce acorresponding integrity check value of the first type.

The method continues at step 904 where the processing module issues aset of write slice requests to a set of storage units, where the set ofwrite slice request includes the set of encoded data slices and acorresponding set of integrity check values of the first type. Theissuing includes generating each write slice request to include one ormore encoded data slices, one or more corresponding integrity checkvalues of the first type, and an identifier to identify the firstintegrity check value function; and sending the write slice requests tothe set of storage units.

When verifying integrity of the received encoded data slice, the methodcontinues at step 906 where a storage unit calculates an integrity checkvalue of a second type for the encoded data slice. The verifyingincludes the storage unit calculating the integrity check value of thefirst type for the received encoded data slice to produce a calculatedintegrity check value, and indicating a favorably verified encoded dataslice when the calculated integrity check value substantially matchesthe corresponding received integrity check function. The calculating ofthe integrity check value of the second type includes selecting thesecond integrity check value function, performing a correspondingselected one-way deterministic function on one or more of the receivedencoded data slice, the received slice name of the encoded data slice,and the received revision number of encoded data slice to produce thecorresponding integrity check value of the second type, and storing theintegrity check value of the second type.

In step 908, when the data is to be recovered, the storage unit issues aread response to the processing module when verifying integrity of arecovered encoded data slice utilizing the integrity check value of thesecond type. The issuing includes retrieving the encoded data slice,recalculating the integrity check value of the second type, indicatingfavorably verified integrity of the retrieved encoded data slice whenthe recalculated integrity check value of the second type substantiallymatches a retrieved integrity check value of the second type, generatingthe read response to include the retrieved encoded data slice, theintegrity check value of the second type, and an identifier of thesecond integrity check value function; and sending the read response tothe processing module, where the processing module disperse storageerror decodes a decode threshold number of received and verified encodeddata slices for each set of encoded data slices to reproduce the data.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other computing devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of the dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: calculating, utilizing a first integritycheck value function, an integrity check value of a first type for eachencoded data slice of a set of encoded data slices to produce acorresponding set of integrity check values; issuing, via a network, oneor more sets of write slice requests 1-n to a set of storage units 1-nwithin the DSN, where the one or more sets of write slice requestsinclude a plurality of sets of the encoded data slices and acorresponding plurality of sets of the integrity check values; and whenverifying integrity of a received encoded data slice, a storage unitcalculating, utilizing a second integrity check value function, anintegrity check value of a second type for the encoded data slice; andwhen data is to be recovered, the storage unit issuing a read responseto a DSN processing unit when the storage unit verifies integrity of arecovered encoded data slice utilizing the integrity check value of thesecond type.
 2. The method of claim 1, wherein the data is divided intoa plurality of data segments, and wherein each data segment is dispersedstorage error encoded to produce a corresponding set of encoded dataslices of a plurality of sets of encoded data slices.
 3. The method ofclaim 1, wherein the calculating an integrity check value of a firsttype includes any of: selecting the first integrity check valuefunction, performing corresponding selected one-way deterministicfunction on one or more of: the encoded data slice, a slice name of theencoded data slice, or a revision number of encoded data slice, toproduce a corresponding integrity check value of a first type.
 4. Themethod of claim 3, wherein the first integrity check value functionincludes any of: a predetermination, interpreting system registry info,interpreting a request, or interpreting available processing resources.5. The method of claim 3, wherein the selected one-way deterministicfunction includes any of: a hash function, a sponge function, or a maskgenerating function.
 6. The method of claim 1, wherein the issuingincludes: generating each of the one or more sets of write slicerequests to include one or more encoded data slices, one or morecorresponding integrity check values, and an identifier to identify thefirst integrity check value function and sending, via the network, thewrite slice requests to the set of storage units.
 7. The method of claim1, wherein the verifying includes: calculating the integrity check valueof the first type for the received encoded data slice to produce acalculated integrity check value and indicating favorably verified whenthe calculated integrity check value substantially matches acorresponding received integrity check value.
 8. The method of claim 7,wherein the calculating the integrity check value of the second typeincludes: selecting the second integrity check value function,performing a corresponding selected one-way deterministic function onone or more of the encoded data slices, a slice name of the encoded dataslice, a revision number of encoded data slice to produce acorresponding integrity check value of the second type, and storing theintegrity check value of the second type.
 9. The method of claim 8,wherein the second integrity check value function is any of: apredetermination, interpreting system registry info, interpret arequest, or interpreting available storage unit processing resources.10. The method of claim 1, wherein the storage unit issuing a readresponse includes: retrieving the encoded data slice, recalculating theintegrity check value of the second type, indicating a favorableverified integrity when the recalculated integrity check value of thesecond type substantially matches a retrieved integrity check value ofthe second type, generating the read response to include the retrievedencoded data slice, the integrity check value of the second type, and anidentifier to identify the second integrity check value function, andsending the read response to the DSN processing unit, and where the DSNprocessing unit dispersed storage error decodes a decode thresholdnumber of received verified encoded data slices for each set of encodeddata slices to reproduce the data.
 11. A dispersed storage network(DSN), the DSN comprises: one or more DSN storage units; and a DSNprocessing module operably coupled to the one or more DSN storage units,wherein the DSN processing module functions to: calculate, utilizing afirst integrity check value function, an integrity check value of afirst type for each encoded data slice of a set of encoded data slicesto produce a corresponding set of integrity check values; issue, via anetwork, one or more sets of write slice requests 1-n to a set of theone or more DSN storage units 1-n, where the one or more sets of writeslice requests include a plurality of sets of the encoded data slicesand a corresponding plurality of sets of the integrity check values; andwhen verifying integrity of a received encoded data slice, a DSN storageunit calculates, utilizing a second integrity check value function, anintegrity check value of a second type for the encoded data slice; andwhen data is to be recovered, the DSN storage unit issues a readresponse to the DSN processing module when the DSN storage unit verifiesintegrity of a recovered encoded data slice utilizing the integritycheck value of the second type.
 12. The computing device of claim 11,wherein the data is divided into a plurality of data segments, andwherein each data segment is dispersed storage error encoded to producea corresponding set of encoded data slices of a plurality of sets ofencoded data slices.
 13. The computing device of claim 11, wherein thecalculating an integrity check value of a first type includes any of:selecting the first integrity check value function, performingcorresponding selected one-way deterministic function on one or more of:the encoded data slice, a slice name of the encoded data slice, or arevision number of encoded data slice, to produce a correspondingintegrity check value of a first type.
 14. The computing device of claim13, wherein the first integrity check value function includes any of: apredetermination, interpreting system registry info, interpreting arequest, or interpreting available processing resources.
 15. Thecomputing device of claim 13, wherein the selected one-way deterministicfunction includes any of: a hash function, a sponge function, or a maskgenerating function.
 16. The computing device of claim 11, wherein theissuing includes: generating each of the one or more sets of write slicerequests to include one or more encoded data slices, one or morecorresponding integrity check values, and an identifier to identify thefirst integrity check value function and sending, via the network, thewrite slice requests to the one or more set of DSN storage units. 17.The computing device of claim 11, wherein the verifying includes:calculating the integrity check value of the first type for the receivedencoded data slice to produce a calculated integrity check value andindicating favorably verified when the calculated integrity check valuesubstantially matches a corresponding received integrity check value.18. The computing device of claim 17, wherein the calculating theintegrity check value of the second type includes: selecting the secondintegrity check value function, performing a corresponding selectedone-way deterministic function on one or more of the encoded dataslices, a slice name of the encoded data slice, a revision number ofencoded data slice to produce a corresponding integrity check value ofthe second type, and storing the integrity check value of the secondtype.
 19. The computing device of claim 18, wherein the second integritycheck value function is any of: a predetermination, interpreting systemregistry info, interpret a request, or interpreting available DSNstorage unit processing resources.
 20. The computing device of claim 11,wherein the DSN storage unit issuing a read response includes:retrieving the encoded data slice, recalculating the integrity checkvalue of the second type, indicating a favorable verified integrity whenthe recalculated integrity check value of the second type substantiallymatches a retrieved integrity check value of the second type, generatingthe read response to include the retrieved encoded data slice, theintegrity check value of the second type, and an identifier to identifythe second integrity check value function, and sending the read responseto the DSN processing module, and where the DSN processing moduledispersed storage error decodes a decode threshold number of receivedverified encoded data slices for each set of encoded data slices toreproduce the data.